Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

Chengbin Hou; Hairong Lv; Jianye Xue; Jinbao Wang; Tianyu Zhang; Xinyu Lin

REVIEW 4 major objections 5 minor 1 cited by

Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that weak, missing, or inaccurate text descriptions in knowledge graphs are a bottleneck for node importance estimation (NIE), and that the bottleneck can be cleared by having a large language model rewrite each node's…

desk verdict A solid, reproducible first LLM-augmentation method for NIE, with a real label-leakage question on the music benchmark; deserves peer review with a control experiment. read the letter →

arxiv 2412.00478 v1 pith:HZ3A6SJS submitted 2024-11-30 cs.AI cs.CL

Xinyu Lin , Tianyu Zhang , Chengbin Hou , Jinbao Wang , Jianye Xue , Hairong Lv This is my paper · ORCID

classification cs.AIcs.CL

keywords nodeimportanceestimationknowledgegraphslargelanguagemodelssemanticaugmentationgraphneuralnetworkstripletsamplingtextembeddingspromptengineering

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that weak, missing, or inaccurate text descriptions in knowledge graphs are a bottleneck for node importance estimation (NIE), and that the bottleneck can be cleared by having a large language model rewrite each node's description from a diverse sample of the node's neighboring triplets. The proposed LENIE pipeline clusters the triplet sentences attached to a node, picks one triplet from each cluster so the language model sees varied relations and entity types, and prompts the model to produce a richer description that merges the graph's facts with its own world knowledge. Feeding these augmented descriptions into existing GNN-based importance estimators improves their predictions across three real-world knowledge graphs. For a reader, the point is that semantic augmentation is a transferable performance boost for NIE that does not require changing the downstream architecture.

What carries the argument

The engine of the method is the clustering-based triplet sampler. Each node's one-hop triplets are converted to sentences, embedded with a text encoder, and clustered, and the sentence nearest each cluster center is chosen so that the LLM's prompt covers diverse relation and entity types instead of being dominated by frequent relations. This diverse triplet text is concatenated with the node's original description (when one exists) inside a node-specific adaptive prompt, and the LLM's generated description becomes the node's initial semantic embedding for the downstream GNN. The clustering step is what distinguishes LENIE from random triplet sampling in the ablations.

What would settle it

Re-run LENIE with entity names anonymized — replace 'Dinosaur', '2 Fast 2 Furious', and the artist names with generic placeholders while keeping the same triplets and prompts — and compare downstream NIE accuracy against the named version. If most of the gains on TMDB5K and MUSIC10K disappear, the LLM is recalling label-relevant popularity knowledge rather than providing neutral semantic augmentation.

Watch

Extended reading notes

Core claim

LENIE is, to the authors' knowledge, the first method to bring large language models into node importance estimation. Its contribution is a semantic-augmentation layer that sits before any GNN-based estimator: extract all one-hop triplets of a node, cluster the embeddings of the triplet sentences, sample the triplet closest to each cluster center, build a node-specific adaptive prompt that combines the sampled triplets with any existing description, and have the LLM generate an enriched description that initializes the node's embedding. On FB15K, TMDB5K, and MUSIC10K, plugging LENIE into the RGTN and LICAP estimators improves nearly every regression and ranking metric, with the largest gains on MUSIC10K, the dataset that originally lacks node descriptions; the paper reports this as the new state of the art.

Load-bearing premise

The reported gains rest on the premise that the LLM's pretrained knowledge of these real-world entities does not already encode the popularity, pageview, or familiarity scores used as training labels, so the generated descriptions add neutral semantics rather than leaking the answers.

Editorial extensions

If this is right

The same augmented descriptions can initialize any of the seven NIE models tested, so the improvement transfers across architectures rather than belonging to one estimator.
Datasets that entirely lack node descriptions, such as MUSIC10K, see the largest gains, making the approach a practical remedy for description-poor knowledge graphs.
Selecting one triplet per semantic cluster keeps prompts short while preserving coverage, which reduces the number of LLM calls and input tokens needed for high-degree nodes.
Because LENIE is a preprocessing layer, improving the underlying LLM should translate directly into better NIE performance without altering the graph-model training procedure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension would be an anonymization control: replacing entity names with placeholders before prompting would reveal whether the LLM is contributing neutral semantic context or recalling popularity knowledge encoded during pretraining.
The sampling-and-augmentation recipe is not tied to NIE; the same triplet-diversity prompt could enrich node descriptions for other KG tasks such as link prediction or node classification.
Because the paper uses a fixed 768-dimension sentence-transformer encoder to embed the LLM's outputs, the results suggest that a strong LLM can compensate for a modest embedding backbone, lowering the deployment cost of semantic augmentation.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: LENIE is a sensible, well-ablated first attempt to use LLMs for semantic augmentation in node importance estimation. The clustering-based triplet sampling is the most genuinely novel piece. The paper deserves review, but the headline gains on MUSIC10K need an extra control before the SOTA claim is fully safe.

What is actually new: this is the first work to bring LLM-generated descriptions into NIE, and the clustering-based triplet sampling strategy is a reasonable mechanism for keeping prompt context diverse. The code and processed datasets are public, which is real evidence and should be credited. The paper also runs a fairly complete ablation chain: vanilla vs. concatenation vs. random sampling vs. clustering, plus three different LLMs and seven downstream models.

Where the paper does well: the experimental coverage is broad and the ablations mostly support the design choices. Clustering-based sampling usually beats random sampling, and the case study in Section V-F shows genuine semantic repair (correcting a wrong director name). The authors are also transparent that MUSIC10K lacks descriptions and that the LLM contributes extra world knowledge—that framing is honest.

Soft spots, in order of importance. First, the label-leakage worry is real. Llama3.1 knows artist popularity and Wikipedia pageviews, and the largest gains appear exactly where the label is familiarity and the only input is the entity name. The Section V-F case study demonstrates the LLM actively injects parametric knowledge, so there is no reason to think it stops at factual relations and never mentions popularity. An anonymized-entity control, or an out-of-distribution set the LLM cannot know, would separate semantic augmentation from target leakage. Second, no paired significance tests are reported; on FB15K the RMSE gap is 0.010 with fold standard deviations around 0.016, so that headline improvement is within noise. Third, the learning-rate selection protocol is ambiguous—the paper says the rate with lowest RMSE was chosen, but not whether that selection happened on validation folds. If it happened on the same test folds, the reported numbers are optimistically selected.

These are real issues, but they are fixable and they do not sink the central direction. The qualitative conclusion—LLM augmentation helps, especially when original descriptions are weak—is likely correct. I would send this to peer review, asking for the control, significance tests, and a clarification of the hyperparameter protocol. The reader's conditional verdict is appropriate.

Referee Report

4 major / 5 minor

Summary. The paper proposes LENIE, a framework that uses LLMs to generate augmented textual descriptions of nodes in knowledge graphs for the downstream task of node importance estimation (NIE). LENIE first extracts one-hop triplets for each node, samples a subset using a clustering-based strategy in the embedding space, then builds node-specific prompts that combine the sampled triplets with any existing node description. An LLM (default Llama3.1-8B) generates an augmented description, which is encoded by a sentence transformer and used as the initial node embedding for GNN-based NIE models such as RGTN and LICAP. Experiments on FB15K, TMDB5K, and MUSIC10K report improvements over existing NIE baselines across five metrics, and the paper claims state-of-the-art performance on all three datasets. The paper also includes ablations of the augmentation components, a comparison of different LLMs, and a study applying LENIE to several downstream models.

Significance. If the central claim is sound, the paper makes a useful empirical contribution by showing that LLM-generated semantic augmentation can improve NIE on knowledge graphs, especially for graphs where node descriptions are missing or thin. The release of source code and processed datasets is a concrete strength that supports reproducibility. The paper is also one of the first to combine LLM text generation with GNN-based NIE, and the framing around KG semantic deficiencies (insufficient, missing, or inaccurate descriptions) is clear and actionable. However, the significance of the empirical claim depends on ruling out a leakage mechanism in which the LLM's pretrained world knowledge encodes the same popularity/familiarity signals that serve as training labels; the current experiments do not provide that control.

major comments (4)

[Section III-D, Section IV-A, Table II] The main empirical claim is vulnerable to label leakage through the LLM's parametric memory. The prompts in Section III-D use real entity names, and the task labels defined in Section IV-A are Wikipedia pageviews (FB15K), TMDB popularity scores (TMDB5K), and artist familiarity (MUSIC10K). Llama3.1 is pretrained on text that contains exactly these popularity and familiarity signals, so the LLM can write rank-correlated content (e.g., 'popular', 'renowned', 'critically acclaimed') into the augmented descriptions. The largest gains occur on MUSIC10K, where entity names are the only node input and SPEARMAN rises from 0.459 (RGTN) to 0.655 (LENIE), which is precisely the setting in which leakage is most plausible. The paper provides no control experiment, such as anonymizing entity names, replacing them with placeholders, or testing on entities outside the LLM's pretraining knowledge. Without such a control, the reported SOTA improvements cannot be attributed unambiguously to semantic augmentation rather than to the LLM recalling the target variable.
[Section V-A, Table II] No significance tests or confidence intervals are reported for the headline comparisons, and several differences are small relative to the reported standard deviations. For example, on FB15K the RMSE improvement of LENIE over RGTN is 0.7826±0.0158 versus 0.7926±0.0176, and on OVER@100 LENIE is actually worse (0.498±0.0331 versus 0.502±0.0194). The paper states that LENIE 'achieved the new state-of-the-art performance' without a paired statistical test across the five cross-validation folds. The authors should report paired tests or confidence intervals for each metric, especially for FB15K where the absolute gains are around one percentage point or less.
[Section IV-D, Section V-A] The hyperparameter selection procedure appears to use the same cross-validation folds for both model selection and final performance reporting: the learning rate with the lowest RMSE is chosen and the corresponding results are recorded as final outcomes. This is a form of test-set reuse unless a nested or separate validation split is used. The manuscript should clarify whether the reported numbers are selected on the test folds, and if so, provide an unbiased evaluation protocol or a correction such as nested cross-validation.
[Section V-B, Table III] The ablation study does not fully isolate the source of the improvement. LENIE(concat), which excludes the LLM, is worse than Vanilla RGTN on FB15K (RMSE 0.8213 versus 0.7926) while improving on TMDB5K and MUSIC10K; LENIE(random) and LENIE(cluster) add the LLM, so any gain could come either from the LLM's parametric knowledge or from the richer textual input. On MUSIC10K, LENIE(random) and LENIE(cluster) are statistically indistinguishable on RMSE (0.0855±0.0042 versus 0.0856±0.0035), so the clustering contribution is not clearly supported on the dataset with the largest headline gain. Additional controls—for example, comparing LLM-augmented descriptions against human-written descriptions of comparable length, or against LLM descriptions generated from anonymized triplets—are needed to attribute the improvement to clustering-guided semantic augmentation.

minor comments (5)

[Table III and Table V headings] The headings 'The performance of LINIE' should read 'LENIE'.
[Abstract and Section VI] There are several typos, including 'sematic deficiencies' in the conclusion and 'descrpitons' in Section V's question list; the manuscript should be proofread.
[Section III-D and Table III] The notation 'LENIE (concate)' in Table III differs from 'LENIE (concat)' in the text; please use consistent naming.
[Section V-A and Figure 3] The text refers to Figure 3 for the comparison of triplet sampling strategies, but the figure itself is not rendered in the submitted text; please ensure the figure is included and readable.
[Section IV-A, MUSIC10K] The dataset description says 'This dataset lacks node description text, so entity names serve as it,' which is awkward; please rephrase for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: LENIE is an empirical augmentation pipeline evaluated against external baselines; no derived quantity reduces to its inputs by construction.

full rationale

I examined the claimed derivation chain: triplet sampling (Eqs. 1-6), prompt construction (Eqs. 7-9), embedding initialization (Eq. 10), and downstream GNN training (Eq. 11) are all standard supervised components; the importance labels enter only as training targets for the downstream regressor, not as inputs to the LLM augmentation or to the triplet sampler. The clustering-based sampler (Eqs. 4-6) uses an off-the-shelf text encoder and K-means on triplet sentences; it does not fit any parameter to the importance labels. The augmented descriptions are encoded with the same text encoder used for clustering, but this is a representation-choice tie, not a reduction of the predicted importance to the clustering input: the clustering selects text, the LLM generates new text, and the GNN learns a mapping from embeddings to labels. The only self-citations are to LICAP [16] (co-authored by several of the present authors) as a baseline, as a source of experimental settings, and as a downstream NIE model; the central SOTA claim is also supported against external methods RGTN, GENI, GraphSAGE, GCN, RGCN, LR, and MLP, so the self-citation is not load-bearing. A legitimate validity concern remains: Llama-3.1 may have pretraining knowledge correlated with the popularity/familiarity labels (especially for MUSIC10K artist familiarity), and the paper lacks an anonymized-entity control; however, that is a label-leakage/correctness risk, not a circularity in which an equation or fitted parameter is equivalent to its input by construction. No quoted step exhibits such a reduction, so no circular step is reported.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method introduces no new theoretical objects. Its free parameters are the triplet sample count k (per dataset) and the learning rate. Its main assumptions are the reliability of LLM knowledge, the validity of the embedding space for clustering, and the transferability of semantic embeddings to GNN training. The unstated assumption about label leakage is the most concerning, but it is captured under soundness rather than here.

free parameters (2)

k (number of sampled triplets per node) = 10 (FB15K), 5 (TMDB5K), 3 (MUSIC10K)
Hand-picked according to average node degree in Section IV-D; not optimized on a validation set and no sensitivity analysis is reported.
learning rate = One of [0.1, 0.5, 0.01, 0.05, 0.001, 0.005, 0.0001, 0.0005] chosen by lowest RMSE
Selected per model and dataset by lowest RMSE (Section IV-D); the protocol may select on the test fold, risking optimistic bias.

assumptions (3)

domain assumption The LLM's generated descriptions are semantically accurate and contain useful information beyond the original KG without leaking target importance labels.
Invoked in Section III-D and in the conclusion that LENIE 'addresses semantic deficiencies'. This is the load-bearing premise that the entire pipeline rests on.
domain assumption Semantic clustering of triplet sentences in the all-mpnet-base-v2 embedding space selects diverse and representative triplets.
Section III-C assumes k-means on sentence embeddings yields clusters whose centers correspond to distinct relationship types or neighboring node types, and that the closest sentence to each center is a good sample.
standard math Node embeddings initialized from the text encoder capture semantics useful to GNN-based importance regression.
Standard transfer-learning assumption throughout Section III-E; the GNN aggregates these embeddings and the text encoder is used as a feature extractor.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs." pith.science (2026). https://pith.science/paper/HZ3A6SJS

@misc{pith2026241200478,
  author       = {Pith},
  title        = {Pith review of: Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HZ3A6SJS}},
  note         = {Machine review of arXiv:2412.00478}
}

read the original abstract

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE models. To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs' extra knowledge and ability of integrating knowledge from both LLMs and KGs. To this end, we propose the LLMs Empowered Node Importance Estimation (LENIE) method to enhance the semantic information in KGs for better supporting NIE tasks. To our best knowledge, this is the first work incorporating LLMs into NIE. Specifically, LENIE employs a novel clustering-based triplet sampling strategy to extract diverse knowledge of a node sampled from the given KG. After that, LENIE adopts the node-specific adaptive prompts to integrate the sampled triplets and the original node descriptions, which are then fed into LLMs for generating richer and more precise augmented node descriptions. These augmented descriptions finally initialize node embeddings for boosting the downstream NIE model performance. Extensive experiments demonstrate LENIE's effectiveness in addressing semantic deficiencies in KGs, enabling more informative semantic augmentation and enhancing existing NIE models to achieve the state-of-the-art performance. The source code of LENIE is freely available at \url{https://github.com/XinyuLin-FZ/LENIE}.

Figures

Figures reproduced from arXiv: 2412.00478 by the authors.

**Figure 2.** The overview of the proposed framework. LENIE extracts diverse semantic information from the given KG, generates [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Comparison of triplet texts extracted by the clustering-based and random-based strategies. The clustering-based [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: A case study of LENIE’s semantic augmentation in three scenarios. Above the dashed line is the semantic information [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
cs.AI 2025-12 conditional novelty 4.0 of 10

A dual-channel model (body title MetaHGNIE) scores node importance in heterogeneous knowledge graphs slightly above prior hypergraph baselines on four benchmarks, but several gains are within reported standard deviati...

Reference graph

Works this paper leans on

38 extracted references · 20 canonical work pages · cited by 1 Pith paper

[1]

A survey on network node ranking algo- rithms: Representative methods, extensions, and applications,

J. Liu, X. Li, and J. Dong, “A survey on network node ranking algo- rithms: Representative methods, extensions, and applications,” Science China Technological Sciences, vol. 64, no. 3, pp. 451–461, 2021

work page 2021
[2]

Estimating node importance in knowledge graphs using graph neural networks,

N. Park, A. Kan, X. L. Dong, T. Zhao, and C. Faloutsos, “Estimating node importance in knowledge graphs using graph neural networks,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 596–606

work page 2019
[3]

Traffic node importance evaluation based on clustering in represented transportation networks,

X. Huang, J. Chen, M. Cai, W. Wang, and X. Hu, “Traffic node importance evaluation based on clustering in represented transportation networks,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 9, pp. 16 622–16 631, 2022

work page 2022
[4]

The pagerank citation ranking: Bringing order to the web

L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999

1999
[5]

Topic-sensitive pagerank,

T. H. Haveliwala, “Topic-sensitive pagerank,” in Proceedings of the 11th international conference on World Wide Web , 2002, pp. 517–526

work page 2002
[6]

Random walk with restart: fast solutions and applications,

H. Tong, C. Faloutsos, and J.-Y . Pan, “Random walk with restart: fast solutions and applications,” Knowledge and Information Systems , vol. 14, no. 3, pp. 327–346, 2008

work page 2008
[7]

On the centrality in a graph,

J. Nieminen, “On the centrality in a graph,” Scandinavian journal of psychology, vol. 15, no. 1, pp. 332–336, 1974

work page 1974
[8]

The centrality index of a graph,

G. Sabidussi, “The centrality index of a graph,” Psychometrika, vol. 31, no. 4, pp. 581–603, 1966

work page 1966

Show all 38 references

[9]

Study on centrality measures in social networks: a survey,

K. Das, S. Samanta, and M. Pal, “Study on centrality measures in social networks: a survey,” Social network analysis and mining , vol. 8, pp. 1– 11, 2018

2018
[10]

Centrality measures in complex networks: A survey,

A. Saxena and S. Iyengar, “Centrality measures in complex networks: A survey,” arXiv preprint arXiv:2011.07190 , 2020

2011 arXiv
[11]

A survey on application of knowledge graph,

X. Zou, “A survey on application of knowledge graph,” in Journal of Physics: Conference Series, vol. 1487, no. 1. IOP Publishing, 2020, p. 012016

2020
[12]

Multiimport: Inferring node importance in a knowledge graph from multiple input signals,

N. Park, A. Kan, X. L. Dong, T. Zhao, and C. Faloutsos, “Multiimport: Inferring node importance in a knowledge graph from multiple input signals,” in Proceedings of the 26th ACM SIGKDD International Con- ference on Knowledge Discovery & Data Mining , 2020, pp. 503–512

2020
[13]

Esti- mating node importance values in heterogeneous information networks,

C. Huang, Y . Fang, X. Lin, X. Cao, W. Zhang, and M. Orlowska, “Esti- mating node importance values in heterogeneous information networks,” in 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022, pp. 846–858

2022
[14]

Node importance esti- mation with multiview contrastive representation learning,

L. Liu, W. Zeng, Z. Tan, W. Xiao, and X. Zhao, “Node importance esti- mation with multiview contrastive representation learning,”International Journal of Intelligent Systems , vol. 2023, no. 1, p. 5917750, 2023

2023
[15]

Representation learning on knowledge graphs for node importance estimation,

H. Huang, L. Sun, B. Du, C. Liu, W. Lv, and H. Xiong, “Representation learning on knowledge graphs for node importance estimation,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021, pp. 646–655

2021
[16]

Label informed contrastive pretraining for node importance estimation on knowledge graphs,

T. Zhang, C. Hou, R. Jiang, X. Zhang, C. Zhou, K. Tang, and H. Lv, “Label informed contrastive pretraining for node importance estimation on knowledge graphs,” IEEE Transactions on Neural Networks and Learning Systems, 2024

2024
[17]

Deep structural knowledge exploitation and synergy for estimating node importance value on heterogeneous information networks,

Y . Chen, Y . Fang, Q. Wang, X. Cao, and I. King, “Deep structural knowledge exploitation and synergy for estimating node importance value on heterogeneous information networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 8, 2024, pp. 8302–8310

2024
[18]

Domain-specific knowledge graph construction for semantic analysis,

N. Jain, “Domain-specific knowledge graph construction for semantic analysis,” in The Semantic Web: ESWC 2020 Satellite Events: ESWC 2020 Satellite Events, Heraklion, Crete, Greece, May 31–June 4, 2020, Revised Selected Papers 17 . Springer, 2020, pp. 250–260

2020
[19]

Knowledge graph quality management: a compre- hensive survey,

B. Xue and L. Zou, “Knowledge graph quality management: a compre- hensive survey,” IEEE Transactions on Knowledge and Data Engineer- ing, vol. 35, no. 5, pp. 4969–4988, 2022

2022
[20]

Unifying large language models and knowledge graphs: A roadmap,

S. Pan, L. Luo, Y . Wang, C. Chen, J. Wang, and X. Wu, “Unifying large language models and knowledge graphs: A roadmap,” IEEE Transactions on Knowledge and Data Engineering , 2024

2024
[21]

Knowledge graph refinement: A survey of approaches and evaluation methods,

H. Paulheim, “Knowledge graph refinement: A survey of approaches and evaluation methods,” Semantic web, vol. 8, no. 3, pp. 489–508, 2017

2017
[22]

Gpt-4 technical report,

J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

2023 arXiv
[23]

Llama 2: Open foundation and fine-tuned chat models,

H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y . Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al. , “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023

2023 arXiv
[24]

A survey on in-context learning,

Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, and Z. Sui, “A survey on in-context learning,” arXiv preprint arXiv:2301.00234, 2022

2022 arXiv
[25]

Retrieval- augmented generation for knowledge-intensive nlp tasks,

P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt ¨aschel et al. , “Retrieval- augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 9459–9474, 2020

2020
[26]

Recent advances in natural language processing via large pre-trained language models: A survey,

B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys, vol. 56, no. 2, pp. 1–40, 2023

2023
[27]

A survey on evaluation of large language models,

Y . Chang, X. Wang, J. Wang, Y . Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y . Wang et al. , “A survey on evaluation of large language models,” ACM Transactions on Intelligent Systems and Technology , vol. 15, no. 3, pp. 1–45, 2024

2024
[28]

A review on large language models: Architectures, applications, taxonomies, open issues and challenges,

M. A. K. Raiaan, M. S. H. Mukta, K. Fatema, N. M. Fahad, S. Sakib, M. M. J. Mim, J. Ahmad, M. E. Ali, and S. Azam, “A review on large language models: Architectures, applications, taxonomies, open issues and challenges,” IEEE Access, 2024

2024
[29]

Chatglm: A family of large language models from glm-130b to glm-4 all tools,

T. GLM, A. Zeng, B. Xu, B. Wang, C. Zhang, D. Yin, D. Rojas, G. Feng, H. Zhao, H. Lai et al. , “Chatglm: A family of large language models from glm-130b to glm-4 all tools,” arXiv preprint arXiv:2406.12793 , 2024

2024 arXiv
[30]

Qwen2 technical report,

A. Yang, B. Yang, B. Hui, B. Zheng, B. Yu, C. Zhou, C. Li, C. Li, D. Liu, F. Huang et al. , “Qwen2 technical report,” arXiv preprint arXiv:2407.10671, 2024

2024 arXiv
[31]

Siren’s song in the ai ocean: a survey on hal- lucination in large language models,

Y . Zhang, Y . Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y . Zhang, Y . Chenet al., “Siren’s song in the ai ocean: a survey on hal- lucination in large language models,” arXiv preprint arXiv:2309.01219, 2023

2023 arXiv
[32]

Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement,

R. Yang, J. Zhu, J. Man, L. Fang, and Y . Zhou, “Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement,” Knowledge-Based Systems, vol. 300, p. 112155, 2024

2024
[33]

Chain-of-thought prompting elicits reasoning in large language models,

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V . Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022

2022
[34]

Semi-supervised classification with graph convolutional networks,

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016

2016 arXiv
[35]

Inductive representation learning on large graphs,

W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems, vol. 30, 2017

2017
[36]

Modeling relational data with graph convolutional networks,

M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, proceedings 15 . Springer, ...

2018
[37]

Llm-based multi-level knowledge generation for few-shot knowledge graph completion,

Q. Li, Z. Chen, C. Ji, S. Jiang, and J. Li, “Llm-based multi-level knowledge generation for few-shot knowledge graph completion,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (2024). , vol. 271494703, 2024

2024
[38]

Knowledge graph-enhanced molecular contrastive learning with functional prompt,

Y . Fang, Q. Zhang, N. Zhang, Z. Chen, X. Zhuang, X. Shao, X. Fan, and H. Chen, “Knowledge graph-enhanced molecular contrastive learning with functional prompt,” Nature Machine Intelligence , pp. 1–12, 2023

2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

A survey on network node ranking algo- rithms: Representative methods, extensions, and applications,

J. Liu, X. Li, and J. Dong, “A survey on network node ranking algo- rithms: Representative methods, extensions, and applications,” Science China Technological Sciences, vol. 64, no. 3, pp. 451–461, 2021

work page 2021

[2] [2]

Estimating node importance in knowledge graphs using graph neural networks,

N. Park, A. Kan, X. L. Dong, T. Zhao, and C. Faloutsos, “Estimating node importance in knowledge graphs using graph neural networks,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 596–606

work page 2019

[3] [3]

Traffic node importance evaluation based on clustering in represented transportation networks,

X. Huang, J. Chen, M. Cai, W. Wang, and X. Hu, “Traffic node importance evaluation based on clustering in represented transportation networks,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 9, pp. 16 622–16 631, 2022

work page 2022

[4] [4]

The pagerank citation ranking: Bringing order to the web

L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999

1999

[5] [5]

Topic-sensitive pagerank,

T. H. Haveliwala, “Topic-sensitive pagerank,” in Proceedings of the 11th international conference on World Wide Web , 2002, pp. 517–526

work page 2002

[6] [6]

Random walk with restart: fast solutions and applications,

H. Tong, C. Faloutsos, and J.-Y . Pan, “Random walk with restart: fast solutions and applications,” Knowledge and Information Systems , vol. 14, no. 3, pp. 327–346, 2008

work page 2008

[7] [7]

On the centrality in a graph,

J. Nieminen, “On the centrality in a graph,” Scandinavian journal of psychology, vol. 15, no. 1, pp. 332–336, 1974

work page 1974

[8] [8]

The centrality index of a graph,

G. Sabidussi, “The centrality index of a graph,” Psychometrika, vol. 31, no. 4, pp. 581–603, 1966

work page 1966